Improving
Superscalar Efficiency with Mini-Graph Processing

For decades, performance has driven the direction of computer
development. In recent years, however, secondary design constraints --
power, area, and temperature, to name a few -- that are often at odds
with performance have become increasingly important. For example, the
performance-driven trend of placing increasingly many cores on one chip
demands that each core have more modest power consumption. Even in the
multi-core paradigm, however, single-thread performance is still
critically important. Power constraints have simply turned the quest for
performance into the quest for performance/power efficiency.

This talk introduces mini-graph processing, a technique that improves
the performance efficiency of any superscalar processor. Mini-graphs
are compiler-detected aggregates of multiple instructions that look and
behave like singleton instructions throughout the entire pipeline. A
mini-graph processor exploits instruction fusion to increase the
efficiency of pipeline stages and structures that perform instruction
book-keeping. A superscalar processor enhanced with mini-graphs can
match the performance otherwise achieved with a wider, deeper
superscalar processor. It does so in a more efficient manner:
maintaining smaller superscalar structures, supporting a higher clock
rate, and consuming less energy.

In this talk, I will introduce the concept of mini-graphs, describe the
basic architecture of a mini-graph processor, and discuss static and
dynamic intelligent mini-graph selection algorithms.

Anne Bracy is a doctoral candidate in the Computer and Information
Science department at the University of Pennsylvania. She researches
novel approaches to efficient superscalar design. Prior to her doctoral
studies, Anne was an undergraduate student at Stanford University. Anne
has also been a member of Intel's Microarchitecture Research Lab in
Santa Clara, California since 2005.